Hepatopanereatobiliary (“HPB”) cancers are second among cancer related deaths. While overall survival rates have improved for most cancers, the five-year survival rate for patients with pancreas cancer is about 6%. Surgical candidates with HPB cancers routinely undergo preoperative imaging with CT. Imaging can be used to assess for metastatic disease, and to help determine resectability based on the burden of disease. Unfortunately, the vast majority of patients who ultimately undergo surgery with curative intent will eventually demonstrate recurrent tumor and possibly die from their disease. While CT can evaluate surgical resectability, the probability for a surgical cure or for intraoperative complications preoperatively, is not yet known.
HPB cancers display variable imaging appearances on CT. For pancreatic cancer in particular, they can range from homogeneous and isoattenuating masses to more heterogeneous hypoattenuating tumors encasing adjacent vessels, and can have features that may reflect tumor differentiation and tumor-stromal interactions. However, the dichotomy between isoattenuating or hypoattenuating tumors on CT does not capture the entire heterogeneity of tumors encountered clinically.
Imaging assessment by texture analysis is an emerging methodology to quantitatively assess differences in the border and heterogeneity of tumors and parenchyma, and has shown prognostic significance for breast, lung and colorectal cancers. However, the potential of texture analysis to be used on, for example, pancreatic cancer, has not been explored. Texture analysis (“TA”) can characterize regions of interest in an image by spatial variations in pixel intensities. For example, a smooth or homogeneous image can lack pixel intensity variation and an irregular or heterogeneous image can have many pixel intensities and can be richly textured. In CT images, texture analysis has the potential to quantify regional variations in enhancement that cannot be qualified by inspection. Recent studies describe texture analysis to augment lesion diagnosis and characterization (see, e.g., Reference 1) to predict survival of colorectal cancer patients (see, e.g., References 2 and 3), and to classify hepatic tumors. (See, e.g., Reference 2). Texture analysis of liver parenchyma has been studied for fibrosis detection and correlated with postoperative pathologic findings. (See, e.g., References 4 and 5). However, texture analysis of CT images has not been used to stratify patients at risk during surgery or to predict treatment outcome.
For example in the liver, recurrences after resection of colorectal liver metastases (“CRLM”) occur in up to 75% of patients. Preoperative prediction of hepatic recurrence has not been well studied but can be important as it can inform therapeutic strategies at the time of initial resection aimed at preventing recurrences. TA is an established procedure that quantifies pixel intensity variations (e.g., heterogeneity) on cross-sectional imaging. It is hypothesized that tumoral and parenchymal change predictive of hepatic recurrence in the future liver remnant (“FLR”) can be detected using TA on preoperative CT images.
Approximately 140,000 new cases of colorectal cancer (“CRC”) are diagnosed each year in the United States. (See, e.g., Reference 78). Nearly 25% of patients have CRLM at initial presentation and approximately 50 to 60% will ultimately develop metachronous CRLM. (See, e.g., Reference 79). In selected patients, hepatic resection can be the treatment of choice. Overall recurrence rates, however, can be as high as 75%. Most recurrences involve the liver and nearly one-third of these recurrences are confined to the liver. (See, e.g., References 80-82). Therefore, predicting, identifying and treating hepatic recurrence can be of critical importance.
To date, trials have not shown an overall survival benefit of perioperative systemic chemotherapy administered around the time of hepatic resection for CRLM. Adjuvant hepatic arterial infusion (“HAI”) chemotherapy with combined floxuridine (“FUDR”) and systemic 5-fluorouracil (“5-FU”) has been associated with improved overall survivability (“OS”) as compared to adjuvant 5-FU alone in a randomized trial. (See, e.g., References 83-85). Furthermore, adjuvant HAI with FUDR can be associated with a significant improvement in hepatic disease-free survival (“HDFS”) after hepatic resection. (See, e.g., Reference 86). Thus, preoperative prediction of the risk of hepatic recurrence can identify ideal candidates for HAI. Although many prognostic models utilizing clinical and pathologic variables have been associated with survival and overall recurrence, no marker prognostic of hepatic recurrence has been established. (See, e.g., References 80 and 87-89).
It has been hypothesized that intrahepatic recurrence after liver resection can arise from occult liver metastases that can be present in the liver at the time of resection, but may not be detectable on conventional imaging. (See, e.g., References 90 and 91). Computer-based imaging analyses have the potential to detect visually occult, but clinically relevant changes in liver parenchymal enhancement. Texture features of liver parenchyma on CT imaging can potentially be altered by occult tumors, and can represent a surrogate for later recurrent disease. (See, e.g., References 92 and 93). Recently, a case-matched study showed that TA of preoperative CT was associated with the risk of post-hepatectomy liver insufficiency. (See, e.g., Reference 94). Additionally, it has been have reported that TA can classify pathologically confirmed chronic hepatitis-C activity and liver cirrhosis grades. (See, e.g., Reference 95). These findings provide preliminary evidence that TA can detect radiographically occult underlying microvascular and parenchymal variations in the liver that can, in turn, be related to the risk of hepatic recurrence after resection for CRLM.
TA can also play a role in the assessment of intratumoral heterogeneity, a feature of malignancy related to cell-density, necrosis, fibrosis and hemorrhage. Texture features from contrast-enhanced CT images were used to distinguish gastric cancer subtypes (see, e.g., Reference 96) and related to overall survival in primary colorectal cancer (see, e.g., Reference 97), hepatocellular carcinoma (see, e.g., Reference 98), and colorectal liver metastases. (See, e.g., References 99 and 100). In CRLM, whole tumor imaging morphology assessed by radiologists correlated to pathologic response and survival (see, e.g., Reference 101), suggesting a link between imaging, pathology and survival but this relationship is not well elucidated.
Thus, it may be beneficial to provide an exemplary system, method and computer-accessible medium which can overcome at least some of the deficiencies described herein above.